数据集的制作参考这篇文章：

https://blog.csdn.net/fanzonghao/article/details/81229409

一，读取数据集

import tensorflow as tf
import numpy as np
import pickle
import matplotlib.pyplot as plt
#对于x变成(samles,pixs),y变成one_hot (samples,10)
"""
one-hot
"""
def reformat(dataset,labels,imgsize,C):dataset=dataset.reshape(-1,imgsize*imgsize).astype(np.float32)#one_hot两种写法#写法一labels=np.eye(C)[labels.reshape(-1)].astype(np.float32)#写法二#labels=(np.arange(10)==labels[:,None]).astype(np.float32)return dataset,labels
"""
读取.pickle文件
"""
def pickle_dataset():path='./data/notMNIST.pickle'with open(path,'rb') as f:restore=pickle.load(f)train_dataset=restore['train_dataset']train_label = restore['train_label']valid_dataset = restore['valid_dataset']valid_label = restore['valid_label']test_dataset = restore['test_dataset']test_label = restore['test_label']del restore# print('Training:', train_dataset.shape, train_label.shape)# print('Validing:', valid_dataset.shape, valid_label.shape)# print('Testing:', test_dataset.shape, test_label.shape)train_dataset,train_label=reformat(train_dataset,train_label,imgsize=28,C=10)valid_dataset,valid_label=reformat(valid_dataset,valid_label,imgsize=28,C=10)test_dataset,test_label=reformat(test_dataset,test_label,imgsize=28,C=10)# print('after Training:', train_dataset.shape, train_label.shape)# print('after Validing:', valid_dataset.shape, valid_label.shape)# print('after Testing:', test_dataset.shape, test_label.shape)return train_dataset,train_label,valid_dataset,valid_label,test_dataset,test_label# #测试生成的数据正确不
# def test(train_dataset,train_label):
#     print(train_label[:10])
#     #plt.figure(figsize=(50,20))
#     for i in range(10):
#         plt.subplot(5,2,i+1)
#         plt.imshow(train_dataset[i].reshape(28,28))
#     plt.show()# if __name__ == '__main__':
#     test(train_dataset,train_label)二，三层神经网络代码

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import read_pickle_dataset
import math
#比较预测和真实精度
def accuracy(predictions,lables):acc=np.sum(np.argmax(predictions,1)==np.argmax(lables,1))/lables.shape[0]return acctrain_dataset,train_label,valid_dataset,valid_label,test_dataset,test_label=read_pickle_dataset.pickle_dataset()
print('Training:', train_dataset.shape, train_label.shape)
print('Validing:', valid_dataset.shape, valid_label.shape)
print('Testing:', test_dataset.shape, test_label.shape)
train_subset=10000
batch_size=128
#常量写法
# tf_train_dataset=tf.constant(train_dataset[:train_subset,:])
# tf_train_label=tf.constant(train_label[:train_subset,:])
tf_valid_dataset=tf.constant(valid_dataset)
#tf_valid_label=tf.constant(valid_label)
tf_test_dataset=tf.constant(test_dataset)
#tf_test_label=tf.constant(test_label)
#place_holder
"""
创建placeholder
"""
def creat_placeholder():X = tf.placeholder(dtype=tf.float32, shape=(None, 28 * 28))Y = tf.placeholder(dtype=tf.float32, shape=(None, 10))beta_regu=tf.placeholder(dtype=tf.float32)# tf_train_dataset=tf.placeholder(dtype=tf.float32,shape=(None,28*28))# tf_train_label=tf.placeholder(dtype=tf.float32,shape=(None,10))return X,Y,beta_regu
"""
初始化权重"""
def initialize_parameters():W1 = tf.Variable(initial_value=tf.truncated_normal(shape=(28 * 28, 1024),stddev=np.sqrt(2.0/(28*28))))b1=tf.Variable(initial_value=tf.zeros(1024))W2 = tf.Variable(initial_value=tf.truncated_normal(shape=(1024, 10)))b2 = tf.Variable(initial_value=tf.zeros(10))parameters={'W1':W1,'b1':b1,'W2':W2,'b2':b2}return parameters
"""
前向传播:三层网络
"""
def forward_propagation(X,parameters):W1 = parameters['W1']b1 = parameters['b1']W2 = parameters['W2']b2 = parameters['b2']Z=tf.add(tf.matmul(X,W1),b1)A=tf.nn.relu(Z)output=tf.add(tf.matmul(A,W2),b2)return output
"""
计算误差 含有正则项误差
"""
def compute_cost(beta,Y,Y_pred,parameter):
#计算lossloss=tf.reduce_mean(                              #(10000,10)      (10000,10)tf.nn.softmax_cross_entropy_with_logits_v2(labels=Y,logits=Y_pred))loss+=beta*(tf.nn.l2_loss(parameter['W1'])+tf.nn.l2_loss(parameter['W2']))return loss
"""
构建模型
"""
def model():beta_array=np.logspace(-4,-2,20)X,Y,beta_regu=creat_placeholder()parameters=initialize_parameters()Y_pred=forward_propagation(X, parameters)loss=compute_cost(beta_regu,Y, Y_pred,parameters)#学习率衰减  指数衰减#learning_rate=tf.train.exponential_decayoptimizer=tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(loss)# 预测train_prediction = tf.nn.softmax(Y_pred)valid_prediction = tf.nn.softmax(forward_propagation(tf_valid_dataset, parameters))test_prediction = tf.nn.softmax(forward_propagation(tf_test_dataset, parameters))init = tf.global_variables_initializer()for beta in beta_array:with tf.Session() as sess:sess.run(init)costs = []for step in range(3001):offset=(step*batch_size)%(train_label.shape[0]-batch_size)batch_data=train_dataset[offset:(offset+batch_size),:]batch_label=train_label[offset:(offset+batch_size),:]feed={X:batch_data,Y:batch_label,beta_regu:beta}_,train_predictions,cost=sess.run([optimizer,train_prediction,loss],feed_dict=feed)# if step%500==0:#     costs.append(cost)print('loss ={},at step {},beta={}'.format(cost,step,beta))# corect_prediction = tf.equal(tf.argmax(output, 1), tf.argmax(train_label, 1))# accuarcy = tf.reduce_mean(tf.cast(corect_prediction, 'float'))# feed = {tf_train_dataset: train_dataset, tf_train_label: train_label}# train_accuarcy = sess.run(accuarcy, feed_dict=feed)print('train accuracy={},beta={}'.format(accuracy(train_predictions,batch_label),beta))print('valid accuracy={},beta={}'.format(accuracy(valid_prediction.eval(),valid_label),beta))print('test accuracy={},beta={}'.format(accuracy(test_prediction.eval(), test_label),beta))# plt.plot(costs)# plt.ylabel('cost')# plt.xlabel('iterations ')# plt.show()if __name__ == '__main__':model()

打印结果：